Computer-based method for teaming research analysts to generate improved securities investment recommendations

ABSTRACT

A computer-based method for combining investment recommendations of individual research providers such as stock analysts. The method includes providing a server running a research team management module. A list of individual research providers is displayed on a client node linked to the server network. A research team is generated based on user input including a number of the research providers. Team rules are assigned to the team defining an algorithm for processing recommendations from the members of the team. Recommendations for securities are retrieved for the research providers on the team, and team recommendations are generated by applying the team rules to the recommendations. Team recommendations are reported to the client node for guiding investments. Processing of the individual recommendations may include applying differing weights to the positive and negative recommendations and combining the weighted recommendations, with the weights being user-selected differentiating strengths of members of the research team.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates, in general, to financial data analysismethods and systems, and, more particularly, to computer software,hardware, and computer-based methods for analyzing research data,including buy, sell, hold, and other recommendations for stocks,generated by security or stock analysts or computer generated to provideconsumers of such research data techniques for aggregating the data toimprove investing performance.

2. Relevant Background

There are hundreds of firms who have as their business to provide buy,hold, and sell recommendations on individual securities—“OpinionatedResearch”. There are also many firms that help the potential customersof such research recommendation determine which providers are thebest—“Performance Measurement Firms”.

Securities or stock analysts or “research analysts” are one of the mainresources for information on companies and the desirability of investingin the companies. Research analysts attempt to predict future eventssuch as earnings well in advance of the time the earnings are announcedand may use these predictions and other information such as long-termprospects to provide investment recommendations, sector rating, growthrate and price targets. The role of the security analyst is generallywell-known and includes issuing earnings estimates for securities, otherfinancial estimates concerning future economic events, recommendationson whether investors should buy, sell, or hold financial instruments,such as equity securities, and other predictions. Security analystestimates provided in research reports may include, but are not limitedto, quarterly and annual earnings estimates for companies whether or notthey are traded on a public securities exchange.

While research reports provide large amounts of useful information,there are numerous challenges facing a consumer of the estimates andrecommendations, such as a manager of a mutual fund or an individualinvestor. Analysts typically summarize their search reports with a briefrecommendation on the action an investor should take regarding aparticular investment or stock. The various research analysts, who maybe individual analysts or firms, often will differ in theirrecommendation for a particular company and its stock. For example, oneresearch analyst may provide a buy recommendation while another firm isproviding a sell recommendation. Further, every firm may use its ownrating system to provide its recommendations with one firm using afive-point scale of buy, outperform, neutral, underperform, or avoidwhile another uses a three-point scale of buy, hold, or sell. Yetanother firm may use a similar number of recommendations but usediffering labels for their recommendations such as a five-point scale ofrecommended list, trading buy, market outperformer, market perform, andmarket underperformer. It may be difficult to understand the meaning ofthese various recommendations and to compare recommendations fromdifferent research analysts. As a result, products have been developedto normalize or standardize the various recommendation scales to allowthe recommendations to be compared and, in some cases combined, forreview by consumers.

The quality of an analyst's recommendations may also vary significantly.Several services have been developed to determine the past performanceof research analysts and to provide rankings of their performancerelative to their peers. For example, ranking services exist thatprovide rankings of analysts based on their ability to predict earningsfor companies. Other services provide rankings of analysts by analyzingtheir research reports to determine whether their recommendations suchas buy, hold, and sell have been accurate within a particular stocksector. Most analysts have strengths and weaknesses such as being bettersuited at picking stocks to sell, at predicting earnings but notpredicting larger economic trends, analyzing stock values for certainsized companies, analyzing technology or durable goods, or the like, andthese strengths and weaknesses cause the analysts to provide moreaccurate data in particular investment environments and less accuratedata in others. Currently, the “performance measurement” companies arefocused on picking the “best” research providers for their needs. Theydo not give the research buyer a way to explore the possibility ofresearch provider combinations. Currently the “research aggregators”have taken in different research providers' data. The aggregatorsgenerally analyze the analyst performance and/or the research provider'sperformance. Aggregators use analyst's estimates accuracy and theperformance of their ratings history accuracy to identify the topperforming analysts and research providers.

The research aggregators are focused on the best analyst at estimates orratings accuracy for a stock, sector or geography or the researchprovider and their performance. This is an isolated way of looking atresearch and is not necessarily the best way to research securities, nordoes this satisfy the needs of the head of research or the researchanalyst. The research analyst purchases a “mosaic” of research or inputsto their investment process and it would be valuable to look at thecombinations of data in order to identify top performing “researchteams”. No aggregator looks at the performance of combinations ofresearch providers or creates virtual or synthetic research teams, usinga combination of research providers to form a team based on a series ofrules that the analyst sets.

There are nearly two hundred research firms that provide research onstocks within the United States alone, and at any one time, nearly onehundred of these analysts may be following a particular company's stock.As a result, it is very difficult to select among the numerous analyststo determine whose recommendations to follow at any particular time andfor any particular stock, sector or market. In an attempt to addressthis problem, a number of services collect recommendations from a largeportion of the analyst firms. Some services combine the recommendationsof the analysts such as in a chart that displays the averagerecommendation of all the recommendations for a particular stock. Thisis often called the “consensus” recommendation, but it is actually arelatively naive average that places an equal weight on all analystsregardless of their past performance or industry rankings. Also, theaverage recommendation of all analysts is often not a unanimousconsensus because a buy or positive recommendation often will include anumber of sell or negative recommendations (and vice versa for a sellrecommendation). Some performance measurement firms, like Starmine,create a more sophisticated average estimate and recommendation bygiving contributing analysts with a better track record, more weightthan contributing analysts with a worse track record Even so, theseexisting tools are focused on allowing the research consumer to find thebest research analysts for a particular stock, or to create astock-by-stock consensus, but they do not help the research consumerfind combinations of providers that would outperform the individualproviders.

With the above issues in mind, it may be useful to further explain theuse of much of the securities research data by those in the financialindustry. Asset and money managers such as traditional equity managers(e.g., long-only investors), pension funds, hedge funds, banks, andindividual investors are generally considered “buy-side” consumers ofresearch reports produced by research analysts They purchase investmentresearch in order to make informed investment decisions including buy,sell, and hold decisions on new and existing investments in stocks ofcompanies. Investment research includes qualitative and quantitativedata from independent research analysts or provides and from affiliatedresearch analysts (e.g., “sell-side” analysts with relationships withthe firm or company they are analyzing). As noted above, investmentresearch firms often have specialties such as a particular geographiccoverage, market capitalization, market sector, or the like.

SUMMARY OF THE INVENTION

To address the above and other problems, the present invention providesmethods and systems for creating combinations of research providers, or“teams”. The invention allows the research consumer to explore differentcombination of providers and analyze how that combination performedrelative to the providers themselves or other teams. The system andmethod involves electing a team of research providers or analysts from aset of such providers and then testing or validating the selected teamusing historical market and financial data to determine theirperformance when their recommendations are aggregated according touser-selected weighting and recommendation aggregation rules. The systemand method then utilize the research team as a virtual analyst toprovide investment recommendations for a user-selected set of securitiesin an ongoing manner.

There are many benefits to the investment community behind the researchteam approach. This analysis can be done without the research consumerseeing the actual recommendations of the research providers, which meansthe research and the proprietary data of the research provider isprotected. This also means that the consumer of research can analyze theresearch provider's performance and their team performance beforepurchasing the underlying research from the provider. There is no othersystem in the market that has a team-based approach to ratings historyand performance. Our system is further innovative in that you don't needto purchase the content/research to view the rating history andperformance. There is no system that looks at the performance ofcombination of research providers or creates a virtual or syntheticresearch provider and tracks its historical performance and treats thevirtual or synthetic research provider as a single entity.

Other customer benefits of the research team approach include ademonstrable alpha generation when using a research team approach toresearch selection and research purchase. The customer has documentedproof of the capability of their research methodology, informationsources. This is significant for the customer in helping to satisfy theregulatory requirements of both the FSA and the SEC in justifying theirspending on investment research. The research team system helps providethe quantitative basis behind a given research spend.

Further, the customer can track the performance of the team as easily astracking the changes of one provider. Changes to estimates, targetprice, and ratings are tracked on a team basis, rather than simplylooking at individual analyst or provider or stock. By tracking theteam, rather than simply individual providers, the analyst monitors onevirtual team or synthetic team, rather than a handful of individualproviders. This simplifies the amount of information the analyst has todigest to inform their investment opinion.

The concept of utilizing a team of research providers rather than asingle provider comes from the inventor's realization that teams oftenperform better than individuals in making decisions similar to stockrecommendations and also because individuals often have weaknesses andstrengths that can compliment each other when the team members areselected correctly. For example, one team member may be accurate on buyrecommendations while another team member may be accurate on sellrecommendations, and weighting and team aggregation rules (e.g.,typically not a simple averaging although average weighting may be usedin some cases) are used to properly combine the members' recommendationsto generate an aggregated or combined recommendation that is moreaccurate over time and in differing investment environments than eitherindividual In the methods and system of the invention, a team member'srecommendations related to their strengths are generally weighted moreheavily than their weaknesses such as weighing their positive ornegative recommendations more heavily.

More particularly, a computer-based method is provided for processingand combining investment recommendations of individual researchproviders (e.g., stock analysts, quantitative models that generaterecommendations, and the like) to achieve improved investmentperformance. The method includes providing a server or computer devicethat runs a research team management module and that is communicativelylinked to a network such as the Internet. A list of individual researchproviders or identifiers of such providers is provided or displayed on aclient node that is linked to the network. The research team managementmodule then may generate a research team that includes two or more ofthe research providers, and the team members typically are chosen by auser of the client node by entering selections in a user interface suchas web page or screen. The method further includes assigning team rulesto the research team to define an algorithm or method of processingrecommendations from the research providers or team members on theresearch team. Then recommendations for one or more securities areaccessed or retrieved for the research providers on the team and a teamrecommendation is generated by applying the team rules to the retrievedrecommendations. The team recommendation is reported to the client nodeto assist a user in making investment decisions.

There are several variables and inputs to creating a team includingselecting research team members and requiring the provider to have anopinion in order to be included in the team rating. Another variable orinput may include the designation of the rule used to calculate therecommendation and recommendation history; this may include but is notlimited to average, majority, consensus, unanimous to buy and one tosell, unanimous to sell and one to buy and unanimous to buy and one tosell but not short. Additional conditions or rules applied to the teaminclude the number of team members who must provide a rating andweightings on attributes such as over weighting a team member's positiveor negative ratings. As a function of the rule and weights a userselects, they will impact and change the research team history andperformance.

The algorithm for processing the individual recommendations may includefirst applying weights to each of the recommendations and then combiningor “averaging” the weighted recommendations, with the weights beinguser-selected to differentiate the strengths of each member of theresearch team (e.g., by applying differing weights on positive andnegative recommendations for an individual provider or differing weightson the various team members). The team rules may also include otheraggregation methods such as determining if more than half of the teammembers have recommended a buy/positive or a sell/negativerecommendation and if so, using this majority recommendation as the teamrecommendation. In some cases, the team rules will call for all to agreeto generate a positive or a buy recommendation and allow one team memberto cause the team to generate a negative or sell recommendation (e.g.,unanimous to buy and one to sell). The method also calls for running aperformance analytics module on the server to determine historicperformance for recommending securities of the set of research providersand delivering at least a portion of this to the client node for use inselecting team members. The selection of one or more of the team membersmay be automated or partially automatic as a user can request high-endperformers in a particular performance category (e.g., as determined bya particular performance analysis methodology). The method may fartherinclude determining the historic performance of the formed research teamby accessing actual prior recommendations of the team members over aparticular time period for a select or default set of stocks orsecurities. This historical team performance can then be reported to theclient node along with historic performance data for the individual teammembers, and a user can then determine if the team members performbetter together or apart and adjust the team rules/members asappropriate (e.g., an iterative process may be used to enhance the teamresults). In addition to such team validation or testing, the researchteam may be used to track a set of securities going forward and alertsmay be generated when one or more of the recommendations of the teammembers is changed causing the team recommendation for a stock orsecurity to also change.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a computer system or networkaccording to an embodiment of the invention showing use of a virtualsecurities analyst system, e.g., a server or other computing device toimplement software modules or programs and stored digital data toperform the research data analysis functions of the invention;

FIG. 2 is a flow diagram illustrating an embodiment of research teamselection and operation according to an embodiment of the invention suchas may be achieved during operation of the system of FIG. 1;

FIG. 3 is a user interface or screen shot of a browser page generated aspart of implementing an embodiment of the invention, e.g., operation ofGUI generation module and performance analytics module of FIG. 1,illustrating a user's or a consumer's ability to select among a numberof performance analysis methodologies to rate independent researchproviders relative to their peers and/or market benchmarks;

FIG. 4 illustrates a user interface or screen shot of a browser pagegenerated as part of an implementation of the invention showing anexemplary performance chart for one performance analysis methodology orrating scheme for independent research providers that shows providersbased on their ability to more accurately pick or recommend securitybuys rather than sells;

FIG. 5 is a user interface similar to that shown in FIG. 4 illustratinganother performance chart for another performance analysis methodologyor rating scheme for independent research providers that shows providersrated against their peers based on a batting average of their pastrecommendations;

FIG. 6 illustrates a user interface or screen shot of a browser page ofa GUI generated as part of an implementation of the invention showing aninput window for allowing a user or consumer to provide input to selecta research team from a group of independent research providers and toestablish rating weights for each of their recommendations and to setteam rules for making a team recommendation or to act as a virtualsecurities analyst providing an aggregated recommendation for aparticular security;

FIG. 7 is a graph illustrating the alpha or differential obtained by useof an exemplary research team as a virtual securities analyst based ontheir 5-point recommendations over a representative time period;

FIG. 8 is a graph with explanatory text showing a report of an exemplaryresearch team with the performance chart comparing performance of theresearch team relative to its three component research providersconsidered individually;

FIG. 9 is a data flow diagram illustrating components of a system orcomputer network of the invention (such as but not limited to the systemof FIG. 1) showing data flow and functions of the system during itsoperation during initial team selection and validation and also duringuse of the team to obtain ongoing recommendations; and

FIG. 10 is a system flow diagram similar to that of FIG. 9 showing dataflow and functions of a system according to the invention during teamselection, team testing, and ongoing recommendation operations.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is directed to methods and systems for generatingand utilizing a research team from a set of securities researchproviders or analysts to provide a team recommendation for securitiessuch as stocks on a watch or coverage list. In practice, a buy-sideanalyst such as money or asset manager uses the tools provides by theinvention as a “team manager” to help them identity combinations ofresearch providers that perform better as a team than as individuals.Without the tools provided by the invention including the team testingor validation module or process, it would be nearly impossible to selectand test such a research team, e.g., an analytics engine in someembodiments may perform 3.6 million data points (calculations) in aminute in order to generate performance ratings or results forindividual research providers and for formed research teams. Once aresearch team is formed, the systems of the invention can track changesin recommendations provided by a research team (e.g., therecommendations of a virtual securities analyst) as easily as trackingchanges in recommendations of an individual research provider. Whileaveraging of recommendations may be useful in some applications, customrules, such as favoring one analyst's or researcher's recommendationsfor buys over other team members and favoring another analyst's sells,allows the user or customer of the embodiments of the invention toleverage each team member's strengths within the research team andgenerate an alpha in its stock or securities investments, i.e., anamount of performance that exceeds a particular benchmark that may bedetermined on a risk-adjusted basis.

The functions and features of the invention are described as beingperformed, in some cases, by “modules” that may be implemented assoftware running on a computing device and/or hardware. For example, theresearch team selection, testing, and use processes or functionsdescribed herein may be performed by one or more processors or CPUsrunning software modules or programs such as an analytics engine togenerate provider performance, a team creation engine to allow a user toselect and test a research team, a rules manager, and the like. Themethods or processes performed by each module is described in detailbelow typically with reference to flow charts or data/system flowdiagrams that highlight the steps that may be performed by subroutinesor algorithms when a computer or computing device runs code or programsto implement the functionality of embodiments of the invention. Further,to practice the invention, the computer, network, and data storagedevices and systems may be any devices useful for providing thedescribed functions, including well-known data processing and storageand communication devices and systems such as computer devices or nodestypically used in computer systems or networks with processing, memory,and input/output components, and server devices configured to generateand transmit digital data over a communications network. Data typicallyis communicated in a wired or wireless manner over digitalcommunications networks such as the Internet, intranets, or the like(which may be represented in some figures simply as connecting linesand/or arrows representing data flow over such networks or more directlybetween two or more devices or modules) such as in digital formatfollowing standard communication and transfer protocols such as TCP/IPprotocols.

The following description begins with a description of one usefulembodiment of a computer system or network 100 with reference to FIG. 1that can be used to implement the research team generation, validation,and use processes of the invention. Representative processes are thendiscussed in more detail with reference to the method 200 of FIG. 2 withsupport or more detail provided by the screen shots of a user interfaceor pages shown in FIGS. 3-6 that may be generated during operation ofthe system 100 of FIG. 1 or another system according to the invention.The description then proceeds to explain the advantages provided by useof a research team created according to the invention to make investmentdecisions with reference to the graphs and reports of FIG. 7 and 8. FIG.9 and 10 provide system and data flow diagrams 900 and 1000 that providefurther explanation of the workings of representative systems of theinvention including their software modules run on typical servers orother computer devices, e.g., a web server accessible via the Internetor other wired or wireless digital communications network.

FIG. 1 illustrates a simplified schematic diagram of an exemplarycomputer system or network 100 and its major components (e.g., computerhardware and software devices and memory devices) that can be used toimplement an embodiment of the present invention. As shown, the system100 includes a virtual securities analyst system 110 that may comprise aserver such as a web server or the like that is connected to a digitalcommunications network 104 such as the Internet, an Internet, or thelike. Such an arrangement allows client nodes 160 that run web browsersor similar applications to use a user interface or graphical userinterface (GUI) 164 to access and interact with the analyst system 110.As shown in FIG. 3-6, a user or operator of the nodes 160 may beprovided one or more research team screenshots 168 generated by thesystem 110 to review performance data on analysts, to select a researchteam from these analysts, to select a set of stocks or other securitiesto watch or cover, to obtain recommendations on these stocks from the“virtual” analyst via the recommendations of the team that are combinedbase on weightings and aggregation rules, and/or to otherwise provideuser input and receive output such as the reports shown in FIG. 7 and 8.The connection to the network 104 also allows the analyst system 110 toaccess server 150 that has memory 152 storing market data 156 such asstock prices and other data from financial markets such as stockexchanges and services that track securities.

The analyst system 110 includes a processor or CPU 112 that runs a setof software modules (that may be implemented partially or fully withhardware in some cases) to provide its functionality. Specifically, theprocessor 112 runs a performance analytics module 114 that providesamong other functions the ability to analyze the performance of aplurality of research providers or analysts that provide recommendationson securities (e.g., buy, sell, hold, and other recommendations onstocks or other securities). The module 114 may determine suchperformance and rate each analyst or provider in relation to their peersusing a rating methodology or historical performance technique. Theinvention is not limited to a particular performance analysis techniqueor methodology 116 with the more important aspect being that a user ofthe client node 160 is able to see ratings of the providers or analystssuch as on a screenshot 168 of GUI 164, in some cases select themethodologies to use to analyze the performance, and to select from theanalysts for their research team using the ratings or performanceresults provided by the analytics module 114. Further, these same ordiffering methodologies 116 may be used to test a formed research teamto determine if the team is able to beat or out-perform individual teammembers and/or market benchmarks. The studies 116 may be those presentlyknown by those skilled in the financial analysis fields or ones laterdeveloped, and in one embodiment, the analytics for determining aresearch providers performance include: consistently outperformingpeers, better at buys than sells, batting average, comparing convictionof rating with return, independent research versus investment bankresearch, size of research coverage universe versus returns, andcomparing type of analysis, philosophy, or research methodology. Thesemethodologies are explained in more detail with reference to FIGS. 2-6,but, again, other methodologies (e.g., performance measurements acceptedby the financial industry to identify “best” performing analystsincluding qualitative measurements, momentum performance measurements,short stock pickers, and the like) may be include in ratingmethodologies 116 to determine historical performance of an analyst in avariety of financial environments and based on varying benchmarks.Memory 130 is provided in the system 110 and is used to store theresearch providers' performance 132 determined by the module 114 (e.g.,ratings of each research analyst in an available set of analysts basedon, for example, their ability to accurately pick stocks to buy orstocks to sell).

The performance data 132 may also include an identifier or listing foreach research provider for who research information including investmentrecommendations is available. A research team selection module 120 isalso run by the processor 112 to enable a user of client node 160 toform teams 136 that are stored in memory 130 and that include two ormore of these research providers indicated in performance data 132 orelsewhere in memory 130 (or accessible by processor 112). Selection of aresearch team 136 via module 120 is an important aspect of the inventionas it allows a user to select, such as via GUI 164, two or more researchproviders or analysts to be members 138 of their team or teams 136, andthese members 138 act to provide a set of investment recommendationsthat are combined to form team recommendations 146 that are also storedin memory 130. The recommendations of the individual members 138 of eachteam 136 are combined to form team (or virtual securities analyst)recommendations 146 using team rules 140, which typically includeweights to be applied to each analyst's recommendations and aggregationrules for determining how to combine the recommendations (as isexplained in more detail with reference to FIG. 6). The team rules 140preferably are selected or adjusted based on input from a user of theclient node 160 but also may be set to default values.

The system 110 further includes a securities selection module 124 thatallows a user such as a money/asset manager or independent investor tochoose a set of securities or stocks 142 that is stored in memory 130.Then, the system 110 may operate to determine the recommendations 146 ofthe team (or teams) 136 for this set of securities 142 (e.g., stocks ina mutual fund, stocks being considered for addition or deletion from aportfolio or fund, or the like) and to watch for changes to suchrecommendations 146 (at which point an alert may be sent to the clientnode 160 via GUI 164 or via other massaging techniques such as e-mails,text massaging, voice massaging, or the like). In some cases, the set ofsecurities 142 and a particular time period is selected by a user ofnode 160 prior to determining the research providers performance 132 bythe analytics module 114, and this allows a user to determine theperformance of the analysts and potential team members based onparticular stocks such as stocks in a particular industry, stocks forcompanies involved in a particular technology or having a particulargeographic coverage, or other distinguishing characteristics.

As will become clear, the research team 136 may also be tested orvalidated by determining their performance for all covered securities byoperating the analytics module 114 or for just the set of securities 142of interest to a user. If a team 136 does not perform well (e.g.,outperform a particular benchmark or better than its members' individualrecommendations), the user can provide input to the system 110 via theGUI 164 to modify the team 136 or to create a new team 136 withdiffering members 138, which can be tested or validated based on a testusing historical performance data (e.g., based on past recommendationsof the team members 138, combining those recommendations into teamrecommendations 146, and determining a resulting performance relative tosome particular benchmark such as market indexes, individual analysts,or the like). A GUI generation module 128 is also included in the system110 and run by the processor 112 to generate the GUI 164 and its screenshots or displays 168 and to provide data from memory 130 or othersources to the node 160.

From the description of the system 100, it will be understood that oneof the aspects of the invention is to allow an asset or money manager orother user/operator accessing the system 110 to find the best or anuseful combination of research providers or analysts that perform betteras a team than as individuals and that even, in some cases, outperformthe “star” or higher-performing individual research providers oranalysts. Such teams 136 have a set of team rules 140 that may bedefault rules or be selected by the user/operator of node 160 to causeeach of the team members 138 to contribute in a desirable manner, e.g.,by having each member play to their strengths as indicated by historicperformance measurements and/or ratings against their peers. Withapplication of the team rules 140, the teams 136 can be thought to actsomewhat like a committee (or single, virtual security analyst) witheach committee or team member 138 providing one vote as to what the teamrecommendation 146 should be for a particular security.

In one embodiment, the team selection module 124 is useful when combinedwith the performance analytics module 114 because a user or operator ofthe client node 160 can be allowed to model or form a team 136 and testor validate it based on historic recommendations and the resulting teamperformance but without actually having access to the individualrecommendations of the team members on any one stock or security. Forexample, an asset manager or other user generally operates with a fixedor limited budget for purchasing research from analysts, and they areforced to select a limited number of research providers and paysubscription or other fees for those analysts' information andrecommendations. With the present invention, the asset manager canoperate the client node 160 before making the purchase decision to modelone or more teams 136 and determine their performance on a default setof securities or a set of securities 142 selected by the asset managerusing historic market data 156 and prior recommendations regarding thosesecurities by the team members 138 via operation of the analytics module114 by processor 112. The use of the processor 112 to run the analyticsmodule or engine 116 allows millions of recommendations over selectedtime periods (e.g., buy and sell recommendations, upgrades, downgrades,and the like) for thousands of securities (e.g., there are over 5,000stocks available on the exchanges in the United States) to be processedaccording to the methodologies 116 to determine prior performance ofindividuals and of a hypothetical or proposed team 136, which would beimpractical and nearly impossible without a fairly robust computingdevice or system.

After the asset manager identifies a useful team 136, the asset managermay decide to use their budget to purchase rights to the research of theanalysts on the team 136 and begin to obtain team recommendations 146for present investment decisions (i.e., based on current recommendationsof the team). Note, the team rules 140 are used to form the “useful” oroutperforming team 136 and would typically be used to process currentindividual recommendations to obtain current team recommendations 146(although this is not required and the team rules 140 may be alteredover time to try to enhance the team recommendations 146 and performanceachieved using such recommendations 146). The securities selectionmodule 124 may be used to help a user of node 160 to select a set ofsecurities 142, as discussed above, and, in so-me embodiments, it isalso adapted to use the team 136 as part of a stock screener orscreening tool to rate or provide recommendations on stocks input to theteam or to retrieve stocks that the team recommends by processing theteam recommendations 146 to obtain all positive recommendations. Analert service module may also be provided such as part of the GUIgeneration module 128 to monitor the team recommendations 146 on anongoing or periodic basis and when an upgrade, downgrade, or other eventoccurs for one of the team members 140 to determine new teamrecommendations 146. When the recommendations 146 for the team 136 areeffected, an alert such as an e-mail, a text message, a voice mail, orother alert may be communicated to a user of the node 160 or otherconsumer of such an alert service (e.g., alert delivered via node 160and/or another communication device such a wireless communicationdevice).

FIG. 2 illustrates an exemplary research team formation and use process200 according to the invention, and the process 200 will be discussedwith reference to FIG. 1 as it may be implanted by operation of thesystem 100 and with reference to FIGS. 3-8 which provide interfaces orpages and reports that may be generated as part of process 200 to enableuser input and to provide output or products from the system 110 to auser of a client node 160. The process or method 200 starts at 204 suchas with loading of the modules of an analyst system 110 on one or morecomputing devices and by providing access to market data 156 to theanalyst system 110 to allow performance measurements to be calculated bythe analytics module 114. At 204, client nodes 160 may also be providedaccess to the analyst system 110, e.g., to allow investors to select aresearch team 136. At 210, the method 200 continues with the building ofa database of historic performance information for a set or number ofindividual research providers (e.g., those firms or individual analyststhat can be chosen to be as members 138 of teams 136). In someembodiments, the performance measurements are determined based on one ormore rating methodologies 116 while in some embodiments step 210 is notperformed until performance measures are requested by a user such bymaking a query via a GUI 164 on a node 160.

With this in mind, the method 200 continues at 220 with the analystsystem 200 functioning to provide at the client node 160 a list ofindividual research providers along with all or subsets of the historicperformance for such providers. For example, the GUI generation module128 may act to provide one or more research team screenshots 168 on GUI164 in response to a user querying the system 110 for information onwhich stock analysts and/or research providers are available as teammembers 138 and for which performance measurements have been determinedor can be readily determined by analytics module 114. For example, FIG.6 illustrates a screenshot 600 of a representative page that may bedisplayed on the client node 160 through operation of the research teamselection module 120 and the GUI generation module 128 (and, in somecases, a browser or similar application on client node 160). Page orscreenshot 600 will be described in more detail below but for now it isuseful to note that a build team window 630 is included that allowsmembers to be listed and added, such as by selection of button 640 witha keyboard, mouse, or other input device and positioning of icon 350.

To determine which analysts from the set of available analysts toinclude on a team 136, it is often useful to review their priorperformance as determined at 210 to identify their strengths andweaknesses. As part of step 220, all or subsets of such performancemeasurements is provided or reported to a requesting user. FIG. 3illustrates a screenshot or web page 300 that the system 110 may presentto a user of a node 160 as part of performing step 220. In screen 300,the frame indicates that a user has chosen provider selection 310 andanalysis or analytical tools 312 within this selection 310. The user caralso choose, such as by positioning of icon 350 and input on a userinput, to view the list of available individual research providers at314, choose to view their previously formed research teams 136 at 316,and/or choose to view their set of securities or coverage lists at 318.With reference to step 220, the window 320 shows a list of performancemeasurements that the user can request for display in a subwindow ofwindow 320 or in another page or screen shot (and, in some cases, run byanalytics module 114), and these measurements may correspond or build onthe ratings methodologies 116.

As discussed, a variety of performance rating and evaluationmethodologies 116 may used to assist a user of client node 160 inselecting team members 138 for a team 136. Typically, a team willoutperform its individual members considered separately with a properset of team rules 140 but better teams are often achievable by selectinganalysts or providers that are among the strongest in a particularcategory or are among the best with regard to a particular performancemethodology. With this in mind, a user may view the window 320 andselect one of the subsets of performance measurements or results of thelisted methodology. These methodologies include an analyst thatconsistently outperforms their peers at 322, which generally involvesthe performance analytics engine 114 determining which researchproviders have outperformed their peers (or at least the peers in theavailable list of analysts at 314) for a particular period of time suchas a recent period (e.g., last 3 to 6 months) or over a longer period oftime (e.g., last 1 to 3 or more years). When 322 is selected, a listing,report, table, chart, or other report is typically transmitted from thesystem 110 to the requesting client node 160 for display at 168 on GUI164 or for outputting as a hard or electronic copy.

Another rating methodology is shown at 324 to be determining whichanalysts are better at buy or positive recommendations than at sell ornegative recommendations. This is significant because many firms rarelyissue a truly negative recommendation due to conflicts of interest orother issues, and as a result, these firms or analysts are generallyunable or are at least slow in predicting when a security should be soldbut are still very competent at making buy recommendations forcompanies. FIG. 4 illustrates a page or screenshot 400 that may beprovided to a requesting client node 160 to display such a performancemeasurement for the available independent research providers. Window 420includes a results chart 421 that shows the performance of a number ofresearch providers with a “best” provider or high performing analystshown at 422 with other providers shown at 426. The ratings or placementof the providers 422, 426 is based in this case on return on positiveratings for the last 5 years on a 5-point scale (or normalization tosuch a scale) and also on return on negative ratings for the last 5years on a similar 5-point scale (which typically will have two negativeratings below a neutral or hold rating or recommendation and twopositive ratings or recommendations above a neutral or hold rating). Asshown, the “Provider1” as shown at 422 outperforms his peers both inregard to return on positive ratings and in regard to return on negativeratings or recommendations. Other providers such as “Provider6”outperform their peers (or median) in regard to their positive ratingsor recommendations while significantly underperforming their peers withregard to their negative ratings or recommendations. Section 423 ofwindow 420 provides details or performance results for a selectedprovider from the chart 421 (i.e., for “Provider1” in this example). Theinformation can be requested by a user of the system 111 for use inselecting one or more team members 138 for their teams 136 and fordeciding what weights to apply to the votes or recommendations of suchteam members 138 and how best to combine the recommendations into anaggregate or combined team recommendation 146. For example, Provider6who is shown to be good at providing positive recommendations but notnegative recommendations may be weighted more for buys than for sellswhile Provider1 who is shown to excel at making both recommendations maybe equally weighted or have a heavier weight than Provider6 for sells(and, optionally, for buys). As will become clear from furtherdescription of FIG. 6 and step 240 of method 200, each team member 138of a team 136 is able to provide both positive and negativerecommendations on any covered stock and a user can assign differentweights for each team member 138 and for each type of recommendation(i.e., positive or negative or, in some cases, neutral).

Referring again to FIG. 3, another methodology 326 involves determiningresearch providers' batting averages. These averages refer to theconcept of a provider making a call (e.g., an upgrade to a buy or adowngrade to a sell or other positive or negative recommendations) anddetermining the percentage of the time that the call is in the rightdirection (e.g., if the call was a positive recommendation did thestock's price increase afterwards, if the call was a negativerecommendation did the stock's price decrease afterwards, and the like).FIG. 5 illustrates a screen or page 500 that may be provided to a clientnode 160 when link 326 is chosen in screen 300 of FIG. 3. As shown, awindow 520 is provided with a performance or results graph 521 thatrates or shows the performance of a number of individual researchproviders or analysts with regard to their batting averages, as may bedetermined by analytics module 114 implementing the “batting average”methodology of performance evaluation. The chart 521 shows the providersbatting average along the X-axis with Provider2 and Provider 4outperforming their peers and the median of all providers. The Y-axis ofchart 521 is used to show the performance measurement of return achievedor achievable by an investor that followed all of the ratings orrecommendations of the same research providers over the past year. Forboth axes, the number of stocks (or “symbols”) tracked in the analysiswas relatively large at over 1,150, which is indicative of the largevolume of calculations that are performed by a performance analyticsmodule 114 of embodiments of the invention to assist a user in selectingappropriate team members 138. As will be appreciated, it may be usefulto have one or two team members 138 on a research team 136 that havehigh batting averages regardless to return as batting average isindicative that their “calls” are in the correct direction and/or it maybe useful to select team members 138 that have both a high battingaverage and a high return as may be shown in chart 521 for providers inthe upper right quadrant. Area 523 of window 420 is used by system 110to deliver or present explanatory information regarding the performanceinformation generated by the analytics module 114.

Using the interface screen 300 of FIG. 3, a user may select otherperformance data generated by the analytics module 114. For example, auser may select a methodology referred to at 328 as “comparingconviction of rating with return” which analyzes whether an analyst'suse of a 3-point scale such as buy, hold, or sell or a 5-point scalethat may add strong buy and strong sell to the 3-point scale makes adifference in returns obtained using their recommendations.Alternatively or in addition, a user may select at 330 an analysisreferred to here as “independent research versus investment bankresearch” that compares the performance of independent researchproviders against the performance of affiliated providers such asinvestment banks that are covering the same stocks or stock sectors(e.g., does independence necessarily lead to better performance?). At332, a user may choose a methodology that looks at the size of theresearch coverage universe versus a provider's returns to try todetermine whether research providers that cover large numbers or smallnumbers of stocks perform better or if there is no discernabledifference. At 334, a user may choose a performance analysis methodology116 that involves comparing types of analysis, philosophy, and researchmethodologies used by various research providers to determine whetherand how such choices may effect performance. Each of these subsets ofperformance information (and others not shown but considered within thebreadth of the invention) may be provided to a user at a client node (orotherwise by delivering a hard or electronic version) at step 220 ofprocess 200 to assist the user in picking the members 138 of a researchteam 136 that may complement each other to provide enhanced combinedrecommendations 146.

Referring again to FIG. 2, the method 200 continues at 230 withreceiving from the client node 160 a user's selection of two or moreindividual research providers 138 to establish a research team 136. Insome embodiments, this will be in response to the user interface screen600 of FIG. 6 or a similar page, form, or interface being generated byGUI generation module 128 and displayed on node 160 as shown at 168 inFIG. 1. The screen or page 600 includes a window 620 that may bedisplayed when “My Research Teams” 316 is selected in the “ProviderSelection” section 310. An area 624 is provided that lists previouslyformed teams at 626 and the team being created or modified (e.g., havingits weighting or aggregation rules changed or adding or deletingmembers) at 628 (e.g., a text box where a default or custom name may beprovided). As can be seen, a single user can create more than one teamas shown in FIG. 1 at 136 and the teams may have the same members 138with differing team rules 140 or may have different members 138 with thesame or different rules (e.g., different rules may be used when the teamis to be used to watch different sets of stocks 142 or for providingrecommendations in differing market conditions or the like).

FIG. 6 shows a region or subwindow 630 to assist a user in buildingtheir team, and as shows, area 632 provides a list of five team members138 that have been selected by a user at step 230 for inclusion on theteam indicated or named at 628. If a user wants to provide additionalmembers 138 (or, in some cases, delete members 138), they may move icon350 to “Add Members” button 640, and at that point, a pull down or otherlisting of the available individual research providers is provided (or auser may type in or otherwise provide a name or identifier for anadditional member) At step 240 of method 200, the system 110 provides auser of a client node 160 the default weighting provided to each teammember and the user provides their settings for these weights (e.g.,acceptance of the default settings and adjustments). In someembodiments, identical weights are applied to both positive and negativerecommendations (e.g., if an advisor's recommendation or rating is givena weight of 25 percent this is used for both buy and sell typerecommendations).

In other embodiments as shown in FIG. 6, a separate weight is applied tothe positive and to the negative ratings or recommendations of eachindividual research provider (although, for some providers, the weightsmay be equal for each type of rating as chosen by a user/defaultvalues). As discussed with regard to the performance analysis of theproviders recommendations, it is often desirable to play to an analyst'sstrengths by weighting tie type of recommendations they are better atproviding more heavily than the recommendations that are not theirstrength, which may even be rated at zero such that a particular type ofrecommendation from that analyst is given no weight (i.e., is notconsidered as part of determining a team recommendation 146). Region 650of window 620 includes settings indicative of rating weights for eachmember of the research team being defined by a user. A column of inputboxes (e.g., pull down boxes or the like) is provided for positiverecommendations 654 and for negative recommendations 658. In oneembodiment, a default value for each member is to have an “average”weight that may be provided in percentages that add up to 100 percent(or weights from 0 to 100 with the total being 100 without any units)but, of course, numerous other weighting algorithms may be used toprovide weighting to each team member's recommendations. For example,with 5 team members as shown on a team, the default weighting would be20 percent for both positive and negative recommendations or ratings fora stock. If the user provides no modifications or inputs, the teammembers' votes or ratings would all be treated equally (e.g., eachreceive “1” vote). However, more typically, the weights are selected toemphasize the strengths of the analysts as identified by the analyzedperformance at step 220. For example, one of the research providers isshown in columns 654, 658 to have equal weighting for each of theirrecommendations but at 25 percent because one provider is not allowed toprovide input or is not considered for positive recommendations.Likewise, two analysts are weighted as zero for negative recommendationsas they may have a history of not accurately picking such ratings basedon a particular performance metric, but they are included in the team tohave their positive recommendations considered in the teamrecommendation. Further, one member is only included for their negativerecommendations and another is included for both recommendations withtheir negative recommendations weighted more heavily (e.g., they arebetter at predicting sells but are also relatively good at buys). Thecombinations of the weightings are nearly infinite with the specificweights shown only being provided as one example and not as alimitation.

At 250, the user of the client node inputs a selection of the team rules140 that are received at the analyst system 110 and used duringvalidation/testing and during use of the team to determine the teamrecommendations 146. Referring to FIG. 6, a team rule entry area 660 isprovided with a text or pulldown box 666 in which a user can view anydefault team rules and select from a list of available rules foraggregating the recommendations of the team members. The team managermodule helps a user construct a research team to emphasize an individualprovider's strength within the team, such as over-weight their buys orsell recommendations. A research team is built by choosing weightings,rules and coverage preferences. The system then generates a history ofbuy, sell and hold recommendations for that team. The team can beplotted on the scatter plot and analyzed against peers, by portfolio, byindustry, sector or security.

Applying team rules in one embodiment involves selection among five rulecategories including: average, majority, consensus, unanimous to buy andone to sell, and unanimous to sell and one to buy. For average, theaverage of the individual providers ratings are calculated in order tocreate a team recommendation. The positive and negative weights of theindividual team member ratings are applied and the average rating iscalculated. For majority, at least half of the team members supplying arating must agree in order to create a team recommendation. The positiveand negative weights of the individual team member ratings are appliedand the majority rating is calculated. For consensus, all team memberssupplying a rating must agree in order to create a team recommendation(i.e., weights do not apply). For unanimous to buy and one to sell, allteam members supplying a rating must agree to a buy for a teamrecommendation of buy, but if one team member goes to sell, then theteam recommendation prompts a sell (i.e., weights do not apply). Weightsare taken into account for the average and majority rules only. Ratingweights do not need to be set for unanimous to buy, one to sell orconsensus. The total for positive or negative weightings is based on theanalyst's preference and while the dialog box has values from 1-100, anypositive integer is valid and numbers greater than 100 are also valid.For example an analyst gives 2× the weight of a single provider,effectively doubling their rating within the aggregate score. Additionalareas an analyst can define in order to produce a research team include:opinion required, and coverage required. For opinion required, theprovider is required to have an opinion in order for there to be a teamrating. For rating coverage, in order for a team rating to be generatedat least X of Y team members must cover the stock for a team to form anopinion. This defaults to a minimum of one team member.

Other team rules may be used that do not use the weights. For example, auser may decide to have the team recommendation determined by a majorityof the team members. When this team rule or recommendation aggregationrule is applied, more than half of the members must agree to either buyor sell (or make a positive or negative recommendation) before such arating or recommendation is generated for the team. Use of this rule maytend to encourage the inclusion of an odd number of team members such as3, 5, 7, or more team members to avoid ties but this is not arequirement. The user may at 250 also decide to use a “consensus” rulein which all must agree to buy or sell (or provide a positive ornegative) recommendation with just one dissenter being allowed to blockthe recommendations of all the other members. Further, a user may selectin box 666 to have the team rule require that a positive or buyrecommendation requires unanimity while only one negative or sellrecommendation may be required to make a negative or sell recommendationfrom the team. With the above discussion understood, otherrecommendation combination rules will be apparent to those skilled inthe art and are considered within the breath of the concept of applyinga team rule to combine the team members' recommendations with or withoutweighting being applied.

Further rules or team settings may be provided such as selection of abox 634 to indicate that a team recommendation cannot be generated ifone or more selected team members does not follow a stock or otherwisehas not provided a recommendation (e.g., certain team members may beconsidered critical to achieving an accurate team recommendation).Similarly, a setting at 670 may be entered by a user to require aparticular member of the team to follow a stock before the team cangenerate a recommendation, and when that number of recommendations fromthe team members is not present the team will not issue a recommendationor issue a statement or report indicating there the stock is notfollowed (e.g., “no recommendation available” or “this stock is notfollowed by a required quorum of the team” or the like). Once themembers are selected and rules and weights set the team can at leasttemporarily be saved in memory 130 by selecting button 680.

The method 200 continues at 260 with validating or testing the researchteam 136 defined by the user based on a default set of securities (e.g.,all securities, a particular subset of securities, or the like) or auser-provided subset of securities (e.g., the set of securities 142defined by the user as ones they wish to track or have coverage such asthose in their fund or considered for addition to their portfolio). Thetesting or validation also is performed over a default time period suchas the past year, past two years, past three to five years, or the likeor a time period selected by the user (e.g., a time period correspondingwith a particular market trend such as a bull market or bear market or aparticular economic environment). The testing or validation may also beperformed based on a default or user-selected methodology 116 such asbatting average, outperforming peers, or the like as discussed abovewith regard to determining performance of analysts at step 220 withperformance analytics module 114. In a testing or validation step 260,the analytics module 114 uses the team weights and team aggregationrules compared to historic market data 156 to determine how the researchteam would have performed based on their actual, historicrecommendations, which are also available in the market/historic data156 (or in a separate database that stores the research of the providersor analysts). For example, the performance of the research team isdetermined for investing in a set of stocks over a particular timeperiod using team recommendations 146 created by retrieving priorrecommendations of the team members 138 for the stocks and generatingteam recommendations 146 using the team rules including weighting andaggregation rules.

At 270, the team's performance and/or recommendations are reported to auser by generating a report or displaying a chart or graph on the clientnode 160. Such reports or charts may provide the team's performance orranking relative to the individual team members, to all availableresearch providers, and/or to market benchmarks. For example, the system110 may generate at 270 an alpha chart 700 as shown in FIG. 7 that canbe provided to the client node 160. As discussed earlier, alpha is ameasure of a differential between the team's performance and a benchmarksuch as a market index. As shown, the alpha chart 700 includes a holdportion 710 in which the research team was able to provide alpha, alphareturn, or, simply, differential return 714 over the index return 712 asmeasured with average returns over time using a 5-point rating orrecommendation scale. Similarly, in underperform and sell portions 720,724 (e.g., negative recommendations), the performance informationindicates the team was able to outperform the market index or provide analpha. Likewise, during positive recommendations of buy and outperform728, 730, the research team's recommendations led to increased returnsor an alpha compared to the market index or benchmark. FIG. 8 shows aresearch team report 800 with a return or performance chart 820 thatshows the research team 822 has outperformed (or provided an alpha) overthe individual research providers 826, which in this example were theindividual members of the team (as shown in the team member overviewprovided in the left hand portion of the report). As shown in this testof the formed research team, the team's recommendations led to betterreturns in the prior 1 and 5 year periods than any of the individualmembers of the team and also provided a better batting average for bothbuys and sells.

The method 200 continues at 274 with a determination of whether the userwishes to modify the team or pick a new team. If so, the method 200returns to 230 (or to 220). If the research team had producedsignificant out performance as shown in chart 700 as shown in FIG. 7 anda report 700 as shown in FIG. 8, the user may decide not to change theteam or its rules, but the user may wish to build another team to try toachieve better performance than that achieved with the existing researchteams or a team that is able to achieve an alpha in particular market orfinancial environments or in a particular stock sector or the like. Inother cases, the user may attempt to slightly modify the rules such asweighting to try to improve the performance of the research team Themethod 200 also may continue at 276 with a determination of whether toretest the team 276, which may be useful to check if the team performsbetter over differing time periods (e.g., over differing economictrends, markets, and the like) or to apply a differing performancemethodology to validate or test the research team using historic marketdata and historic recommendations of the team members. At 278, the usercan select to change the rules of the team, too, prior to retestingwhich returns control to step 240 or can retest at 260 such as bychanging the time period for validation. The method 200 then ends at290.

Use of the formed team is not shown in the method 200 of FIG. 2, but itwill be understood that once a research team is formed that it may beused prospectively to make investment decisions. For example, researchfrom team members may be ordered and processed as a stock screener todetermine when to add new investments to a portfolio or fund. In othercases, the research team, its data or research including stockrecommendations, and the team rules may be processed by system 110 orother modules/systems to track a set of securities 142 and determinewhen stocks should be bought, held, and sold based on the teams currentrecommendations 146 for each of these stocks. Further, alerts may beissued when there is change to one of the members recommendations acall, an upgrade, a downgrade, or the like that effects the teamrecommendations. Further, with reference to the method 200, the user mayhave the option of manually selecting the team members such as afterreviewing the team members' historic performances as discussed above orthe user may choose to have their members chosen based on inputcriteria. For example, a user may choose to add a team member with aparticular ranking when a performance methodology is considered, e.g.,select the highest ranked predictor of sells, the highest ranked battingaverage analyst, the highest ranked momentum analyst, and the like.Further, in some embodiments, the “default” weights may be chosen by thesystem 110 based on the determined performance of each of the teammembers relative to the other members (e.g., an automated weighting tohighlight the strengths and weaknesses of the team members) such as byusing proportions based on the rankings or returns of the team relativeto the other members or the like.

FIG. 9 illustrates a system flow diagram 900 illustrating operation of asystem according to the invention for team selection, management, anduse such as may be achieved with system 100. As shown, a user or clientnode operated by a user 904 interfaces with a system such as byinputting data and viewing reports or outputs of the system. The systemincludes a research provider selection module 910 in which a universe ofavailable symbols or stocks of companies 910 is defined and may includethe stocks of a particular stock exchange(s) or be a larger set or asubset of such stocks (e.g., essentially filtering providers by interestlist or holdings). At 914, the module 910 may allow a user to apply oneor more filters to the universe of symbols and at 918 a dataset of thefiltered subset of symbols is generated, and this allows the user 904 toselect a set of securities or stocks for coverage by a research team andfor use in evaluating performance of individual research providers.

The system includes a research team manager module 930 that the user 904uses at 932 to choose a collection of individual research providers todraft a research team, and, as discussed with regard to FIG. 2, themembers are often selected based on their historic performance orrankings. Each of the drafted teams and their team members are stored inmemory at 933. At 934, the system functions to allow the user 904 tocreate a recommendation rule or team rule for defining how therecommendations of each of the team members will be processed on each ofthe teams to allow a team recommendation to be generated, and the rulesare stored in memory at 935. At 936 a script of the team rule may begenerated and then compiled at 938 for later application torecommendation information for the team members. She research teammanager 930 is shown at 940, 942, and 944 to act to determine fromanalytics data rating or recommendation history 944 recommendations ofboth the individual research providers and the research team on whichthey are members at 942 with team recommendations being determined at940 using rules 938. At 946, the research team manager 930 may act todetermine new or updated recommendations of an individual researchprovider on one of the teams 933, which may be provided by continuousupdates or change detection at 948 in the research provider reports data(e.g., processing of inbound data feeds from a data acquisition group orDAG and/or a document management architecture or DMA) that triggers at949 an update signal or alert.

The system further includes a performance analytics engine 960 that maybe requested at 950 by the research team manager 930 to recalculate teamand/or individual research provider performance or rankings. To thisend, the engine 960 may periodically such as once a day obtain at 962analyst data including recommendations and at 964 the closing price ofstocks, such as those in the dataset defined at 918 and/or that areassociated with research provider recommendations. At 966, stocks thatare being tracked have their prices update and the analytics database isupdated at 968 to reflect performance details based on the providersratings or recommendations. At 9703 it is indicated that the engine 970may be rerun periodically such as once per day or in response to a query950. At 974, the engine 960 functions to update recommendation or ratinghistory tables and performance based on a particular analysismethodology and this information is stored in memory at 978.

In some cases, the methodology employed by the engine 960 to determineteam and individual research provider performance is a totalreturn-based methodology (including, in some cases, dividendreinvestment) that provides meaningful return experiences for directcomparison to other investments, providers, and benchmarks. Themethodology determines out-performance or under-performance for allrecommendations or ratings from buy, sell, and hold periods (e.g., seethe alpha chart of FIG. 7). When combined with scoring or othertechniques, this methodology can provide relative performancecomparisons to determine impact of rating conviction for analysts thatprovide 5-point rating scales as well as other scales such as 3-pointrating scales.

FIG. 10 provides another data flow diagram 1000 that illustrates dataflow during operation of a system according to the invention (such assystem 100 of FIG. 1). As shown, a script engine 1020 provides inputdata/messages to a methodology data calculation module 1040 (e.g.,analytics engine 960 of FIG. 9 or performance analytics module 114 ofFIG. 1 or the like) and rules module 1030. The messages or informationrequired by module 1040 may be provided by market information and/orresearch provider reports or database 1010 and from input from a uservia their Web or other node 1014. The messages/information includespricing updates 1022 regarding monitored stocks (e,g., 5000 or morestocks or a subset of the stock symbol universe). Recommendation updates1024 are also tracked and when an analyst makes a call such as anupgrade or downgrade this information is retrieved by the engine 1020and passed to the module 1040 for updating performance information.

A user may create and update teams with communications 1028 that arepassed by script engine 1020 from the Web browser or client node 1014 tothe research team module 1060 via rules module 1030 that is used toupdate and track team rules such as weighting and aggregation algorithmsfor generating team recommendations and via calculation module 1040 thatuses the teams and its rules to determine team performance and itsrecommendations. Research provider module 1050 is used to provide alisting of available research providers (e.g., from one to 200 or more)and in some cases to provide research provider reports which may also beprovided by module 1010. A user may request at 1026 that the data berolled up or combined to generate performance reports that compare theperformance of a generated research team with its individual members andto report on the teams recommendations and ability to generate alphaover time. The information that is output to the user from thecalculation module 1040 may be considered a wrapped library of data fromthe research team 1080 that is stored in memory.

Although the invention has been described and illustrated with a certaindegree of particularity, it is understood that the present disclosurehas been made only by way of example, and that numerous changes in thecombination and arrangement of parts can be resorted to by those skilledin the art without departing from the spirit and scope of the invention,as hereinafter claimed. For example, the method 200 and flow shown inFIGS. 9 and 10 is not intended to indicate a mandatory order of steps orprocessing, and many of the functions of the invention may be performedin any order and/or may be repeated as useful to better select,validate/test, and use research teams made up of individual researchproviders or analysts. Prior to the invention described herein, therewas no analytics tool or process that generated teams of researchproviders whose recommendations were processed according to customizableweighting and/or rules to generate improved investment recommendations(e.g., buy, hold, sell, and similar recommendations), which generatesignificant alpha relative to benchmarks when they are implemented by amoney or asset manager or other investor in securities. Prior technologywas useful for generating performance data on individual researchproviders based on historical financial data such as priorrecommendations for stocks and the stocks performance after suchrecommendations. For example, the prior performance analysis technologymay have been used to determine a research analyst's such as aninvestment bank's batting average (i.e., consistency), return (i.e.,performance), and the like, but the inventive methods and systemsdescribed in this document were the first to roll up performance toallow a user or customer to create a research team and then apply rulessuch as weighting algorithms and aggregation rules to generaterecommendations that clearly perform better than recommendations of theindividual team members and often better than accepted marketperformance benchmarks.

1. A computer-based method for processing and combining investmentrecommendations from research providers such as stock analysts,comprising: providing a server running a research team management moduleon a digital communications network; providing identifiers for a set ofresearch providers to a client node linked to the communicationsnetwork; with the research team management module, generating a researchteam comprising two or more of the research providers based onselections received from the client node; assigning team rules with theresearch team management module to the research team defining analgorithm for processing recommendations of research providers on theresearch team; accessing recommendations of the research providers onthe research team for a security; generating a team recommendation forthe security by processing the accessed recommendations using thealgorithm defined by the team rules; and reporting the teamrecommendation to the client node.
 2. The method of claim 1, wherein thealgorithm comprises combining the accessed recommendations afterapplying weights to the accessed recommendations that are defined in theteam rules for both positive and negative recommendations for each ofthe research providers on the research team.
 3. The method of claim 2,wherein the weights are user-selected based on input received from theclient node and wherein the weights for the positive recommendationsdiffer from the weights for the positive recommendations of at least oneof the research providers.
 4. The method of claim 1, wherein thealgorithm comprises determining whether more than half of the researchproviders agree on a positive or a negative recommendation and if so,choosing the agreed upon positive or negative recommendation as the teamrecommendation.
 5. The method of claim 1, wherein the algorithmcomprises determining from the accessed recommendations whether all ofthe research providers on the research team have provided a positive ora negative recommendation for the security and if so, providing thepositive or negative recommendation as the team recommendation.
 6. Themethod of claim 1, wherein the algorithm comprises determining from theaccessed recommendations whether all of the research providers on theresearch team have provided a positive recommendation for the securityand if so, providing the positive recommendation as the teamrecommendation.
 7. The method of claim 6, wherein the algorithm furthercomprises determining if any of the accessed recommendations is anegative recommendation, and if so, providing the negativerecommendation as the team recommendation.
 8. The method of claim 1,further comprising running a performance analytics module to determinehistoric performance of the set of research providers for recommendingsecurities and delivering at least a portion of the determined historicperformance to the client node prior to the generating of the researchteam.
 9. The method of claim 8, further comprising running theperformance analytics module to determine historic performance of theresearch team including accessing prior recommendations of the researchproviders on the research team over a time period, applying the teamrules to the prior recommendations to generate historic teamrecommendations, and processing security pricing information with thehistoric team recommendations and the method further comprisingreporting the historic performance of the research team to the clientnode with a comparison to the historic performance of the set ofresearch providers.
 10. The method of claim 8, wherein the historicperformance of the set of research providers is determined based on aperformance analysis methodology user-selected from a set ofmethodologies and wherein the selections for the two or more researchproviders for the research team comprise at least one request for ahighest performer based on one of the methodologies.
 11. The method ofclaim 1, further comprising repeating the accessing, the generating, andthe reporting for a set of securities and yet further comprisingmonitoring for modifications of the recommendations of the researchproviders and when detected generating an alert to the client node witha new recommendation created by repeating the team recommendationgenerating.
 12. A method for forming a team of individual researchproviders to generate stock recommendations, comprising: running a userinterface on a client node linked to a network; displaying performanceinformation based on analysis of prior stock investment recommendationsfor a plurality of research providers in the user interface; receiving aselection of a set of the research providers for a research team;generating team rules for combining stock investment recommendationsfrom the set of research providers on the research team into teaminvestment recommendations; running an analytic module on a server todetermine investment performance for the research team based for aperiod of time based on the team investment recommendations for theperiod of time for a set of stocks; and reporting the investmentperformance to the client node along with individual performanceinformation for the period of time for the set of research providers onthe research team.
 13. The method of claim 12, further operating theanalytic module to determine the performance information for theplurality of research providers based on a user-selected performanceanalysis methodology.
 14. The method of claim 12, wherein the teaminvestment recommendations for the set of stocks comprise positive,neutral, or negative recommendations and the team rules compriseaggregation rules for combining the positive, neutral, or negativerecommendations of the set of research providers on the research team.15. The method of claim 14, wherein the team rules generating comprisesreceiving from the client node weights to apply to each positiverecommendation and each negative recommendation of each of the researchproviders on the research team and wherein the aggregation rulescomprise combining the recommendations after applying the weights. 16.The method of claim 12, wherein team rules are selected from the groupof methods for combining team member recommendations consisting of anaveraging methodology, a majority methodology, a consensus methodology,and a unanimous-to-buy-one-to-sell methodology.
 17. A system forproviding a virtual security analyst providing a single investmentrecommendation for each security in a set of securities based onrecommendations of a set of research providers, comprising: means forenabling a user to specify two or more of the research providers toinclude on a research team; means for enabling a user to specify a setof rules for combining positive and negative recommendations forsecurities generated by the research providers on the research team;means for determining for a set of securities historic performance ofindividual ones of the research providers on the research team and ofthe research team based on the set of rules and prior positive andnegative recommendations of the research providers; and means forreporting the historic performances to a user.
 18. The system of claim17, wherein the set of rules includes separate weight values assigned tonegative recommendations and to positive recommendations for each of theresearch providers on the research team.
 19. The system of claim 17,means for enabling a user to define a plurality of securities forcoverage by the research team, means for determining positive andnegative recommendations of the research providers on the research teamfor the plurality of securities, means for generating teamrecommendations by processing the determined positive and negativerecommendations using the set of rules, and means for reporting the teamrecommendations.
 20. The system of claim 19, means for generatingupdated team recommendations in response to modifications of one or moreof the determined positive and negative recommendations and means foralerting a user to the generated updated team recommendations.
 21. Thesystem of claim 17, wherein the historic performances reported to theuser exclude the prior positive and negative recommendations of theresearch providers, whereby the research team is validated withoutrelease of recommendation information of the research providers on theresearch team.